{"title":"光伏系统最大功率点跟踪的动态神经控制","authors":"A. Dounis, P. Kofinas, C. Alafodimos, D. Tseles","doi":"10.1109/NEUREL.2012.6420029","DOIUrl":null,"url":null,"abstract":"Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that's, the system output error. The feasibility of the proposed neural control is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Dynamic neural control for maximum power point tracking of PV system\",\"authors\":\"A. Dounis, P. Kofinas, C. Alafodimos, D. Tseles\",\"doi\":\"10.1109/NEUREL.2012.6420029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that's, the system output error. The feasibility of the proposed neural control is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.\",\"PeriodicalId\":343718,\"journal\":{\"name\":\"11th Symposium on Neural Network Applications in Electrical Engineering\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"11th Symposium on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2012.6420029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"11th Symposium on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2012.6420029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic neural control for maximum power point tracking of PV system
Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that's, the system output error. The feasibility of the proposed neural control is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.
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